cell_list

Periodic cell-list pair construction.

import mlx_atomistic.cell_list

Classes

PairListStats

class PairListStats
    def __init__(pair_count: int, n_cells: tuple[int, int, int], cell_count: int, occupied_cell_count: int, search_radius: float, estimated_pair_bytes: int, estimated_cell_list_bytes: int, backend: str = 'periodic_cell_list', representation_kind: str = 'pairs', candidate_count: int | None = None, estimated_candidate_bytes: int = 0, compaction_backend: str | None = None, fallback_reason: str | None = None)

Diagnostics for a constructed periodic pair list.

Parameters

Name	Type	Default	Description
pair_count	int
n_cells	tuple[int, int, int]
cell_count	int
occupied_cell_count	int
search_radius	float
estimated_pair_bytes	int
estimated_cell_list_bytes	int
backend	str	'periodic_cell_list'
representation_kind	str	'pairs'
candidate_count	int | None	None
estimated_candidate_bytes	int	0
compaction_backend	str | None	None
fallback_reason	str | None	None

PeriodicCellList

class PeriodicCellList
    def __init__(bins: dict[tuple[int, int, int], np.ndarray], n_cells: tuple[int, int, int], cell_lengths: np.ndarray, search_radius: float, n_particles: int, estimated_bytes: int)

Binned particles for orthorhombic periodic pair construction.

Parameters

Name	Type	Default	Description
bins	dict[tuple[int, int, int], np.ndarray]
n_cells	tuple[int, int, int]
cell_lengths	np.ndarray
search_radius	float
n_particles	int
estimated_bytes	int

Properties

• cell_count int — Total grid cell count.
• occupied_cell_count int — Number of cells containing at least one particle.

Functions

build_periodic_cell_list

def build_periodic_cell_list(positions, cell: Cell, *, search_radius: float) -> PeriodicCellList

Bin wrapped positions into a periodic orthorhombic grid.

Parameters

Name	Type	Default	Description
positions
cell	Cell
search_radius	float

Returns

• PeriodicCellList

build_periodic_pair_list

def build_periodic_pair_list(positions, cell: Cell, *, search_radius: float, sort_pairs: bool = True, max_workers: int | None = None) -> tuple[np.ndarray, PairListStats]

Build deterministic unique i < j pairs within search_radius.

Parameters

Name	Type	Default	Description
positions
cell	Cell
search_radius	float
sort_pairs	bool	True
max_workers	int | None	None

Returns

• tuple[np.ndarray, PairListStats]

estimate_pair_list_bytes

def estimate_pair_list_bytes(pair_count: int) -> int

Return storage bytes for an int32 (pair_count, 2) pair array.

Parameters

Name	Type	Default	Description
pair_count	int

Returns

• int